A head-related transfer function (“HRTF”) is a set of filters which individually describe the acoustic transformation of a sound as it travels from a specific location in space to a listener's ear canals. This transformation is caused by interaural differences in the acoustic transmission path and interactions with acoustic reflections from the head, shoulders, and outer ears. The HRTF represents all of the perceptually relevant acoustic information needed for a listener to determine a direction of sound origin.
Non-directional sounds, when transmitted to the listener, provide no cues as to the direction of sound origin. These otherwise non-directional sounds, with an HRTF applied thereto, may be utilized by virtual auditory display (“VAD”) designers to impart a directional precept. Such capability has a broad range of applications from navigational aids for pilots and the visually-impaired to virtual and augmented reality for training and entertainment purposes.
Yet, the spatially-auditory cues represented by the HRTF are highly individualized. In other words, unique anatomical and spatial differences require a distinct HRTF for each individual to properly perceive the direction of sound origin. Thus, technologies to derive generalized HRTFs from measurements on individuals or acoustic manikins often result in unnatural sounding displays for listeners (i.e., a listener on which the measurements were not made) and result in a greater degree of mislocalization. When faithful reproduction of spatial auditory cues is necessary, HRTFs must be measured or estimated for each specific listener. Unfortunately, accurate measurement of individualized HRTFs by conventional methods requires taking acoustic measurements at a large number of spatial locations around the listener, who is outfitted with miniature, in-ear microphones. The HRTF measurement process requires a large amount of time and expensive equipment, which makes it use cost-prohibitive for many commercial applications.
Other conventional strategies for attaining individual measurements have included building costly and extensive spherical speaker arrays so that measurements can be made more rapidly. Alternatively still, smaller and cheaper movable speaker arrays may be used, but result in significantly longer measurement collection times. Some approaches have utilized a priori information about the HRTF in an attempt to aid interpolation from a generic HRTF to a listener specific HRTF.
While several of these conventional techniques show promising results in terms of reconstruction or modeling error, no explicit localization studies have been conducted to determine the exact number of spatial measurements required to achieve accurate localization. One problem with many of these conventional methods is the lack of a simple HRTF representation, which characterizes all of the perceptually-relevant HRTF features using only a small number of parameters. Personalization techniques could also benefit from more detailed knowledge of exactly how HRTFs differ among individuals, which is currently scarce. Yet, these methods do provide interesting frameworks for HRTF estimation that should, theoretically, be much more fruitful than current results would suggest. Thus, there remains a need for improved methods of personalizing HRTFs having perceptually-relevant information for proper source origin identification.